Representation of local geometry in the visual system
Biological Cybernetics
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Silhouette-Based Isolated Object Recognition through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Retrieving 3D shapes based on their appearance
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Probabilistic Shape-Based Image Indexing and Retrieval
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Unsupervised learning from a corpus for shape-based 3D model retrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Mobile phone-based pervasive fall detection
Personal and Ubiquitous Computing
Who is repinning?: predicting a brand's user interactions using social media retrieval
Proceedings of the Thirteenth International Workshop on Multimedia Data Mining
| Hi-index | 0.00 |
In this paper, we describe the use of differential profiles, which are computed from 2D shapes smoothed with Gaussian functions, as the shape features for building a shape retrieval system. We build a global shape component dictionary from all the shape descriptors collected from shapes available in a database and then represent each shape as a probabilistic mixture of elements from such a dictionary. Finally, shape retrieval from a given database is simply done by comparing the mixing coefficients of the model of a query shape and those of known shapes. Our retrieval experiments are done on both object contour and line drawing collections and show promising results.